Python Data Engineering MCQ Questions with Answers – Page 2 (Latest 2026)
Practice Python Data Engineering MCQ questions with detailed explanations and clear answer validation. These MCQs help you revise core concepts, compare close options, and improve accuracy for interviews, certification exams, and technical screening rounds. Use this updated 2026 set to strengthen fundamentals and confidence.
Q51. Which statement about read_csv / to_csv is most accurate?
Select an answer to check.
Answer: Read and write CSV files.
Here, Read and write CSV files. is the right choice. Many parsing options. It aligns directly with what the question asks about which statement about read_csv / to_csv is most. Competing choices sound plausible, but they miss the key condition.
Q52. How is read_csv / to_csv best characterized?
Select an answer to check.
Answer: Read and write CSV files.
In this case, Read and write CSV files. is correct. Many parsing options. It aligns directly with what the question asks about how is read_csv / to_csv best characterized. Competing choices sound plausible, but they miss the key condition.
Q53. Which option best describes Parquet in Python?
Select an answer to check.
Answer: Columnar binary format for efficient analytics.
The best option here is Columnar binary format for efficient analytics.. Use pyarrow/fastparquet. It aligns directly with what the question asks about which option best describes parquet in python. Competing choices sound plausible, but they miss the key condition.
Q54. What is the primary purpose of Parquet?
Select an answer to check.
Answer: Columnar binary format for efficient analytics.
For this question, Columnar binary format for efficient analytics. is correct. Use pyarrow/fastparquet. It aligns directly with what the question asks about what is the primary purpose of parquet. Competing choices sound plausible, but they miss the key condition.
Q55. Which statement about Parquet is most accurate?
Select an answer to check.
Answer: Columnar binary format for efficient analytics.
Columnar binary format for efficient analytics. is the correct answer here. Use pyarrow/fastparquet. It aligns directly with what the question asks about which statement about parquet is most accurate. Competing choices sound plausible, but they miss the key condition.
Q56. How is Parquet best characterized?
Select an answer to check.
Answer: Columnar binary format for efficient analytics.
Here, Columnar binary format for efficient analytics. is the right choice. Use pyarrow/fastparquet. This matches the core idea being tested around how is parquet best characterized. Competing choices sound plausible, but they miss the key condition.
Q57. Which option best describes PyArrow in Python?
Select an answer to check.
Answer: Apache Arrow Python bindings.
In this case, Apache Arrow Python bindings. is correct. Zero-copy interchange. This matches the core idea being tested around which option best describes pyarrow in python. Competing choices sound plausible, but they miss the key condition.
Q58. What is the primary purpose of PyArrow?
Select an answer to check.
Answer: Apache Arrow Python bindings.
The best option here is Apache Arrow Python bindings.. Zero-copy interchange. This matches the core idea being tested around what is the primary purpose of pyarrow. Competing choices sound plausible, but they miss the key condition.
Q59. Which statement about PyArrow is most accurate?
Select an answer to check.
Answer: Apache Arrow Python bindings.
For this question, Apache Arrow Python bindings. is correct. Zero-copy interchange. This matches the core idea being tested around which statement about pyarrow is most accurate. Competing choices sound plausible, but they miss the key condition.
Q60. How is PyArrow best characterized?
Select an answer to check.
Answer: Apache Arrow Python bindings.
Apache Arrow Python bindings. is the correct answer here. Zero-copy interchange. This matches the core idea being tested around how is pyarrow best characterized. Competing choices sound plausible, but they miss the key condition.
Here, Parallel/distributed pandas-like dataframes. is the right choice. Scales beyond memory. That is exactly the concept behind which option best describes dask in python in this context. Competing choices sound plausible, but they miss the key condition.
In this case, Parallel/distributed pandas-like dataframes. is correct. Scales beyond memory. That is exactly the concept behind what is the primary purpose of dask in this context. Competing choices sound plausible, but they miss the key condition.
The best option here is Parallel/distributed pandas-like dataframes.. Scales beyond memory. That is exactly the concept behind which statement about dask is most accurate in this context. Competing choices sound plausible, but they miss the key condition.
For this question, Parallel/distributed pandas-like dataframes. is correct. Scales beyond memory. That is exactly the concept behind how is dask best characterized in this context. Competing choices sound plausible, but they miss the key condition.
Q65. Which option best describes Polars in Python?
High-performance Rust-based dataframe library. is the correct answer here. Lazy execution and Arrow-based. That is exactly the concept behind which option best describes polars in python in this context. Competing choices sound plausible, but they miss the key condition.
Here, High-performance Rust-based dataframe library. is the right choice. Lazy execution and Arrow-based. It fits the requirement in the prompt about what is the primary purpose of polars. Competing choices sound plausible, but they miss the key condition.
Q67. Which statement about Polars is most accurate?
In this case, High-performance Rust-based dataframe library. is correct. Lazy execution and Arrow-based. It fits the requirement in the prompt about which statement about polars is most accurate. Competing choices sound plausible, but they miss the key condition.
The best option here is High-performance Rust-based dataframe library.. Lazy execution and Arrow-based. It fits the requirement in the prompt about how is polars best characterized. Competing choices sound plausible, but they miss the key condition.
Q69. Which option best describes SQLAlchemy in Python?
Select an answer to check.
Answer: Python ORM and SQL toolkit.
For this question, Python ORM and SQL toolkit. is correct. Core + ORM layers. It fits the requirement in the prompt about which option best describes sqlalchemy in python. Competing choices sound plausible, but they miss the key condition.
Q70. What is the primary purpose of SQLAlchemy?
Select an answer to check.
Answer: Python ORM and SQL toolkit.
Python ORM and SQL toolkit. is the correct answer here. Core + ORM layers. It fits the requirement in the prompt about what is the primary purpose of sqlalchemy. Competing choices sound plausible, but they miss the key condition.
Q71. Which statement about SQLAlchemy is most accurate?
Select an answer to check.
Answer: Python ORM and SQL toolkit.
Here, Python ORM and SQL toolkit. is the right choice. Core + ORM layers. This is the most accurate statement for which statement about sqlalchemy is most accurate. Competing choices sound plausible, but they miss the key condition.
Q72. How is SQLAlchemy best characterized?
Select an answer to check.
Answer: Python ORM and SQL toolkit.
In this case, Python ORM and SQL toolkit. is correct. Core + ORM layers. This is the most accurate statement for how is sqlalchemy best characterized. Competing choices sound plausible, but they miss the key condition.
Q73. Which option best describes Airflow / Prefect in Python?
Select an answer to check.
Answer: Workflow orchestration tools.
The best option here is Workflow orchestration tools.. DAG-based scheduling. This is the most accurate statement for which option best describes airflow / prefect in. Competing choices sound plausible, but they miss the key condition.
Q74. What is the primary purpose of Airflow / Prefect?
Select an answer to check.
Answer: Workflow orchestration tools.
For this question, Workflow orchestration tools. is correct. DAG-based scheduling. This is the most accurate statement for what is the primary purpose of airflow /. Competing choices sound plausible, but they miss the key condition.
Q75. Which statement about Airflow / Prefect is most accurate?
Select an answer to check.
Answer: Workflow orchestration tools.
Workflow orchestration tools. is the correct answer here. DAG-based scheduling. This is the most accurate statement for which statement about airflow / prefect is most. Competing choices sound plausible, but they miss the key condition.
Q76. How is Airflow / Prefect best characterized?
Select an answer to check.
Answer: Workflow orchestration tools.
Here, Workflow orchestration tools. is the right choice. DAG-based scheduling. It aligns directly with what the question asks about how is airflow / prefect best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q77. Which option best describes ETL vs ELT in Python?
Select an answer to check.
Answer: ETL transforms before load; ELT loads then transforms.
In this case, ETL transforms before load; ELT loads then transforms. is correct. Modern warehouses favor ELT. It aligns directly with what the question asks about which option best describes etl vs elt in. The remaining choices fail because they don’t satisfy the full definition.
Q78. What is the primary purpose of ETL vs ELT?
Select an answer to check.
Answer: ETL transforms before load; ELT loads then transforms.
The best option here is ETL transforms before load; ELT loads then transforms.. Modern warehouses favor ELT. It aligns directly with what the question asks about what is the primary purpose of etl vs. The remaining choices fail because they don’t satisfy the full definition.
Q79. Which statement about ETL vs ELT is most accurate?
Select an answer to check.
Answer: ETL transforms before load; ELT loads then transforms.
For this question, ETL transforms before load; ELT loads then transforms. is correct. Modern warehouses favor ELT. It aligns directly with what the question asks about which statement about etl vs elt is most. The remaining choices fail because they don’t satisfy the full definition.
Q80. How is ETL vs ELT best characterized?
Select an answer to check.
Answer: ETL transforms before load; ELT loads then transforms.
ETL transforms before load; ELT loads then transforms. is the correct answer here. Modern warehouses favor ELT. It aligns directly with what the question asks about how is etl vs elt best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q81. Which option best describes a data lake in Python?
Select an answer to check.
Answer: Object storage of raw/curated data.
Here, Object storage of raw/curated data. is the right choice. Often Parquet on S3/GCS. This matches the core idea being tested around which option best describes a data lake in. The remaining choices fail because they don’t satisfy the full definition.
Q82. What is the primary purpose of a data lake?
Select an answer to check.
Answer: Object storage of raw/curated data.
In this case, Object storage of raw/curated data. is correct. Often Parquet on S3/GCS. This matches the core idea being tested around what is the primary purpose of a data. The remaining choices fail because they don’t satisfy the full definition.
Q83. Which statement about a data lake is most accurate?
Select an answer to check.
Answer: Object storage of raw/curated data.
The best option here is Object storage of raw/curated data.. Often Parquet on S3/GCS. This matches the core idea being tested around which statement about a data lake is most. The remaining choices fail because they don’t satisfy the full definition.
Q84. How is a data lake best characterized?
Select an answer to check.
Answer: Object storage of raw/curated data.
For this question, Object storage of raw/curated data. is correct. Often Parquet on S3/GCS. This matches the core idea being tested around how is a data lake best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q85. Which option best describes a data warehouse in Python?
Select an answer to check.
Answer: Analytics-optimized DB (Snowflake, BigQuery, Redshift).
Analytics-optimized DB (Snowflake, BigQuery, Redshift). is the correct answer here. Columnar and parallel. This matches the core idea being tested around which option best describes a data warehouse in. The remaining choices fail because they don’t satisfy the full definition.
Q86. What is the primary purpose of a data warehouse?
Select an answer to check.
Answer: Analytics-optimized DB (Snowflake, BigQuery, Redshift).
Here, Analytics-optimized DB (Snowflake, BigQuery, Redshift). is the right choice. Columnar and parallel. That is exactly the concept behind what is the primary purpose of a data in this context. The remaining choices fail because they don’t satisfy the full definition.
Q87. Which statement about a data warehouse is most accurate?
Select an answer to check.
Answer: Analytics-optimized DB (Snowflake, BigQuery, Redshift).
In this case, Analytics-optimized DB (Snowflake, BigQuery, Redshift). is correct. Columnar and parallel. That is exactly the concept behind which statement about a data warehouse is most in this context. The remaining choices fail because they don’t satisfy the full definition.
Q88. How is a data warehouse best characterized?
Select an answer to check.
Answer: Analytics-optimized DB (Snowflake, BigQuery, Redshift).
The best option here is Analytics-optimized DB (Snowflake, BigQuery, Redshift).. Columnar and parallel. That is exactly the concept behind how is a data warehouse best characterized in this context. The remaining choices fail because they don’t satisfy the full definition.
Q89. Which option best describes partitioning in Python?
Select an answer to check.
Answer: Split data by key for parallel/skip scans.
For this question, Split data by key for parallel/skip scans. is correct. Reduces scan cost. That is exactly the concept behind which option best describes partitioning in python in this context. The remaining choices fail because they don’t satisfy the full definition.
Q90. What is the primary purpose of partitioning?
Select an answer to check.
Answer: Split data by key for parallel/skip scans.
Split data by key for parallel/skip scans. is the correct answer here. Reduces scan cost. That is exactly the concept behind what is the primary purpose of partitioning in this context. The remaining choices fail because they don’t satisfy the full definition.
Q91. Which statement about partitioning is most accurate?
Select an answer to check.
Answer: Split data by key for parallel/skip scans.
Here, Split data by key for parallel/skip scans. is the right choice. Reduces scan cost. It fits the requirement in the prompt about which statement about partitioning is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q92. How is partitioning best characterized?
Select an answer to check.
Answer: Split data by key for parallel/skip scans.
In this case, Split data by key for parallel/skip scans. is correct. Reduces scan cost. It fits the requirement in the prompt about how is partitioning best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q93. Which option best describes file formats in Python?
Select an answer to check.
Answer: CSV, JSON, Avro, Parquet, ORC.
The best option here is CSV, JSON, Avro, Parquet, ORC.. Choose by access pattern. It fits the requirement in the prompt about which option best describes file formats in python. The remaining choices fail because they don’t satisfy the full definition.
Q94. What is the primary purpose of file formats?
Select an answer to check.
Answer: CSV, JSON, Avro, Parquet, ORC.
For this question, CSV, JSON, Avro, Parquet, ORC. is correct. Choose by access pattern. It fits the requirement in the prompt about what is the primary purpose of file formats. The remaining choices fail because they don’t satisfy the full definition.
Q95. Which statement about file formats is most accurate?
Select an answer to check.
Answer: CSV, JSON, Avro, Parquet, ORC.
CSV, JSON, Avro, Parquet, ORC. is the correct answer here. Choose by access pattern. It fits the requirement in the prompt about which statement about file formats is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q96. How is file formats best characterized?
Select an answer to check.
Answer: CSV, JSON, Avro, Parquet, ORC.
Here, CSV, JSON, Avro, Parquet, ORC. is the right choice. Choose by access pattern. This is the most accurate statement for how is file formats best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q97. Which option best describes data quality checks in Python?
In this case, Schema, null, range, uniqueness validations. is correct. Catch bad data early. This is the most accurate statement for which option best describes data quality checks in. The remaining choices fail because they don’t satisfy the full definition.
Q98. What is the primary purpose of data quality checks?
The best option here is Schema, null, range, uniqueness validations.. Catch bad data early. This is the most accurate statement for what is the primary purpose of data quality. The remaining choices fail because they don’t satisfy the full definition.
Q99. Which statement about data quality checks is most accurate?
For this question, Schema, null, range, uniqueness validations. is correct. Catch bad data early. This is the most accurate statement for which statement about data quality checks is most. The remaining choices fail because they don’t satisfy the full definition.
Q100. How is data quality checks best characterized?
Schema, null, range, uniqueness validations. is the correct answer here. Catch bad data early. This is the most accurate statement for how is data quality checks best characterized. The remaining choices fail because they don’t satisfy the full definition.